{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. 加载必要的模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from pandas.core.frame import DataFrame\n",
    "import pandas as pd  \n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from scipy.fftpack import fft,ifft"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. 确保csv数据格式如图所示\n",
    "\n",
    "![csv文件格式](https://raw.githubusercontent.com/techyoung-edu/sleepmonitor/master/acc_csv_format.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. 加载csv数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>acc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19:41:35.193</td>\n",
       "      <td>0.961693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19:41:35.226</td>\n",
       "      <td>0.963778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19:41:35.226</td>\n",
       "      <td>0.966693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19:41:35.263</td>\n",
       "      <td>0.963984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19:41:35.300</td>\n",
       "      <td>0.962294</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           time       acc\n",
       "0  19:41:35.193  0.961693\n",
       "1  19:41:35.226  0.963778\n",
       "2  19:41:35.226  0.966693\n",
       "3  19:41:35.263  0.963984\n",
       "4  19:41:35.300  0.962294"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv(\"39Hz_Accelerometer.csv\",names = [\"time\",\"acc\"])\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. 处理acc数据\n",
    "+ 使用numpy array来对acc_list进行处理\n",
    "+ 把加速度减去1.0,这是传感器静态时默认的加速度。\n",
    "+ 把加速度乘1000,从而使的加速度变成以mg为单位。\n",
    "+ 求出期望和标准差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the acc dist mean:-35.95mg and std:2.13mg\n"
     ]
    }
   ],
   "source": [
    "acc_list = np.array(dataset[\"acc\"])\n",
    "acc_list = (acc_list - 1.0)*1000\n",
    "acc_mean = np.mean(acc_list)\n",
    "acc_std  = np.std(acc_list)\n",
    "acc_mean_str = \"{:.2f}\".format(acc_mean)\n",
    "acc_std_str = \"{:.2f}\".format(acc_std)\n",
    "print(\"the acc dist mean:%.2fmg and std:%.2fmg\" %(acc_mean,acc_std))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. 绘制综合加速度直方分布图\n",
    "+ rcParams设置中文字体\n",
    "```python\n",
    "plt.rcParams['font.sans-serif'] = ['Microsoft JhengHei']  # 用来正常显示中文标签\n",
    "```\n",
    "+ 用axvline绘制±σ区间 参考:https://matplotlib.org/devdocs/api/_as_gen/matplotlib.pyplot.axvline.html\n",
    "+ 直方图的bin宽度是0.1mg(16bit ±2g 的加速度传感器的分辨率也就是0.06mg,我们把bin的宽度设为0.1mg,就可以保证最小的分bin单位)\n",
    "+ 图片大小为16:9,单位了在jupyter notebook里显示完全,先用(8,4.5)和dpi=50来绘制\n",
    "+ 不要设置xlim，这样可以观察出来加速度的分布范围"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x225 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins=np.arange(np.min(acc_list),np.max(acc_list),0.1)\n",
    "plt.figure(figsize=(8, 4.5), dpi=50)\n",
    "ax = plt.subplot()\n",
    "plt.hist(acc_list,bins) \n",
    "plt.title(\"MPU9520 acc distribution still. mean=\"+acc_mean_str+\"mg std=\"+acc_std_str+\"mg\") \n",
    "plt.axvline(x=acc_mean,color=\"green\",linewidth=1)\n",
    "plt.axvline(x=acc_mean-acc_std,color=\"r\",linewidth=1)\n",
    "plt.axvline(x=acc_mean+acc_std,color=\"r\",linewidth=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. 绘制综合加速度直方分布图-xlim设置为-128到127\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bins=np.arange(np.min(acc_list),np.max(acc_list),0.1)\n",
    "plt.figure(figsize=(8, 4.5), dpi=50)\n",
    "ax = plt.subplot()\n",
    "plt.hist(acc_list,bins) \n",
    "plt.title(\"MPU9520 acc distribution still. mean=\"+acc_mean_str+\"mg std=\"+acc_std_str+\"mg\") \n",
    "plt.axvline(x=acc_mean,color=\"green\",linewidth=1)\n",
    "plt.axvline(x=acc_mean-acc_std,color=\"r\",linewidth=1)\n",
    "plt.axvline(x=acc_mean+acc_std,color=\"r\",linewidth=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. 绘制综合加速度violin图\n",
    "+ 低g加速度分析的范围是±128mg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins=np.arange(np.min(acc_list),np.max(acc_list),0.1)\n",
    "plt.figure(figsize=(8, 4.5), dpi=100)\n",
    "ax = plt.subplot()\n",
    "ax.set_xlim(-128, 128)\n",
    "sns.violinplot(x=acc_list)\n",
    "plt.title(\"MPU9520 acc distribution still. mean=\"+acc_mean_str+\"mg std=\"+acc_std_str+\"mg\") \n",
    "plt.axvline(x=acc_mean,color=\"green\",linewidth=0.5)\n",
    "plt.axvline(x=acc_mean-acc_std,color=\"r\",linewidth=0.5)\n",
    "plt.axvline(x=acc_mean+acc_std,color=\"r\",linewidth=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. 绘制综合加速度直方KDE分布图\n",
    "+ 低g加速度分析的范围是±128mg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins=np.arange(np.min(acc_list),np.max(acc_list),0.1)\n",
    "plt.figure(figsize=(8, 4.5), dpi=100)\n",
    "ax = plt.subplot()\n",
    "ax.set_xlim(-128, 128)\n",
    "sns.distplot(acc_list,kde=True,bins=bins,color=\"black\")\n",
    "plt.title(\"MPU9520 acc distribution still. mean=\"+acc_mean_str+\"mg std=\"+acc_std_str+\"mg\") \n",
    "plt.axvline(x=acc_mean,color=\"green\",linewidth=0.5)\n",
    "plt.axvline(x=acc_mean-acc_std,color=\"r\",linewidth=0.5)\n",
    "plt.axvline(x=acc_mean+acc_std,color=\"r\",linewidth=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. 绘制综合加速度直方KDE分布图+小提琴图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x1ba20b4cfd0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc_min = -128\n",
    "acc_max =  128\n",
    "figure,axes = plt.subplots(2,1,figsize = (16,9),sharex = False)\n",
    "axes[0].set_xlim(acc_min, acc_max)\n",
    "axes[1].set_xlim(acc_min, acc_max)\n",
    "plt.title(\"MPU9520 acc distribution still. mean=\"+acc_mean_str+\"mg std=\"+acc_std_str+\"mg\") \n",
    "sns.distplot(acc_list,kde=True,bins=np.arange(acc_min,acc_max,0.1),ax=axes[0])\n",
    "sns.violinplot(x=acc_list,ax=axes[1])\n",
    "axes[0].axvline(x=acc_mean,color=\"green\",linewidth=2)\n",
    "axes[0].axvline(x=acc_mean-acc_std,color=\"r\",linewidth=1)\n",
    "axes[0].axvline(x=acc_mean+acc_std,color=\"r\",linewidth=1)\n",
    "axes[1].axvline(x=acc_mean,color=\"green\",linewidth=2)\n",
    "axes[1].axvline(x=acc_mean-acc_std,color=\"r\",linewidth=1)\n",
    "axes[1].axvline(x=acc_mean+acc_std,color=\"r\",linewidth=1)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
